Mapping of genomic interactions

ABSTRACT

The present invention relates to genomic analysis. In particular, the present invention provides methods and compositions for mapping genomic interactions.

STATEMENT OF GOVERNMENTAL SUPPORT

This invention was supported by grants from the National Institutes ofHealth: NHGRI-ENCODE HG03143; NHGRI-ENCODE HG003129; and CA109597.Consequently, the United States government may have certain rights tothe disclosed technology.

FIELD OF INVENTION

The present invention relates to genomic analysis. In particular, thepresent invention provides methods and compositions for mapping genomicinteractions.

BACKGROUND

Efforts are underway to map genes and regulatory elements throughout thehuman genome. ENCODE-Consortium et al., Science 306:636-640 (2004). Thegoals of these efforts are to identify many different types of elements,including those involved in gene regulation, DNA replication and genomeorganization in general. However, currently thorough identification ofgenes and regulatory elements has only been performed for a selected 1%of the human genome.

In order to fully annotate the human genome and to understand itsregulation, a complete gene map of all functional elements should alsodetermine and define the relationships between them. For instance, foreach gene it needs to be established by which elements it is regulated.This is complicated by the fact that the genomic positions of genes andelements do not provide direct information about functionalrelationships between them. A well-known example is provided byenhancers that can regulate multiple target genes that are located atlarge genomic distances or even on different chromosomes withoutaffecting genes immediately next to them (Spilianakis et al., Nature435: 637-645 (2005); and West et al., Hum Mol Genet. 14:R101-111(2005)).

What is needed in the art is a high-throughput method that can isolateinteractions between genes and gene regulatory elements as well asinteractions between regulatory elements themselves combined withmethods to quantify the occurrence of such interactions.

SUMMARY OF THE INVENTION

The present invention relates to genomic analysis. In particular, thepresent invention provides methods and compositions for mapping genomicinteractions. For example, in some embodiments, the present inventionprovides chromosome conformation capture carbon copy (5C) methods forstudying genomic interactions (e.g., genomic interactions involved inregulation of gene expression and global chromatin structure). Themethods and compositions of the present invention find use in diagnosticand research applications.

Accordingly, in some embodiments, the present invention provides amethod, comprising contacting a genomic interaction library (e.g.,generated using the 3C or related method; for example the 3C methoddescribed by Dekker, et al (2002) Science 295:1306-1311) with aplurality of unique pairs of PCR primers under conditions such thatligation mediated amplification generates a second genomic interactionlibrary; and amplifying the second genomic library with a single pair(or a limited number of pairs) of PCR primers, wherein the PCR primersamplify all members of the second genomic library. In some embodiments,the plurality of unique pairs of PCR primers comprises at least 10,preferably at least 100, more preferably at least 500, even morepreferably at least 1000, yet more preferably at least 10,000 and stillmore preferably at least 100,000 unique pairs of PCR primers. In someembodiments, the second genomic interaction library comprises nucleicacids approximately 100 bps in length, although the present invention isnot so limited. In some embodiments, members of the second genomicinteraction library are identified and/or quantified using highthroughput sequencing. In other embodiments, members of the secondgenomic interaction library are identified using a microarray. In stillfurther embodiments, additional identification methods are employed,including, but not limited to, hybridization analysis, mass spectrometryanalysis, etc. In some embodiments, the genomic interaction librarycomprises sequences involved in long range genomic interactions (e.g.,interaction of activating or repressing chromatin elements with a geneor global genomic structures or interactions between locations ondifferent chromosomes). In some embodiments, the genomic interactionlibrary is derived from a cell (e.g., an animal (e.g., human) cell, abacterial cell, a viral cell, or a plant cell). In certain embodiments,the method further comprises the step of calculating interactionfrequencies for the long-range genomic interactions. In someembodiments, the genomic interaction library is derived from a cell thathas one or more variant genes (e.g., polymorphisms (e.g., singlenucleotide polymorphisms), genomic deletions, genomic fusions, genomictranslocations, or genomic inversions).

The present invention further comprises a method, comprising: contactinga cell with a test compound; and generating a second genomic libraryfrom the cell using the method of the present invention, and comparinginteraction frequencies in the second genomic library with interactionfrequencies in a second genomic library generated from a cell notexposed to the test compound.

The present invention additionally comprises a method, comprising:contacting nucleic acid with a cross-linking agent under conditions suchthat interacting chromatin segments are cross-linked; digesting thecross-linked chromatin segments (e.g., with a restriction enzyme) togenerate digested chromatin segments; ligating the digested chromatinsegments to generate a genomic interaction library; contacting thegenomic interaction library with a plurality of unique pairs of PCRprimers under conditions such that ligation mediated amplificationgenerates a second genomic interaction library; and amplifying thesecond genomic library with a single pair (or a limited set of pairs) ofPCR primers, wherein the PCR primers amplify all members of the secondgenomic library.

In yet other embodiments, the present invention provides a kit,comprising a plurality of unique primers for performing ligationmediated amplification on a genomic interaction library. In someembodiments, the plurality of unique primers comprises at least 10,preferably at least 100, more preferably at least 500, even morepreferably at least 1000, yet more preferably at least 10,000 and stillmore preferably at least 100,000 unique primers. In some embodiments,the kit comprises all of the components necessary or sufficient togenerate and utilize a diagnostic signature or interaction profile(e.g., control signatures and instructions and/or software for comparingthe diagnostic signature to a test sample). In some embodiments, the kitfurther comprises one or more of a polymerase (e.g., a thermostable DNApolymerase), a ligase (e.g., a thermostable ligase), primers foramplifying the products of a ligase chain reaction, buffers, controlreagents, sequencing reagents, solid surfaces for analysis, microarraysfor analysis, detection devices, software, instructions, and controlgenomic interaction libraries.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A-1B shows a schematic representation of 5C. (1A) A schematic ofthe generation of a 5C library from a 3C library (1B) 5C primer design.

FIG. 2A-2E shows analysis of the human beta-globin locus and developmentof 5C. (2A) Schematic representation of the human beta-globin locus.(2B) 3C analysis of interactions between the LCR (HS5) and the rest ofthe beta-globin locus. (2C) Representative 3C library titration insingleplex LMA with 5C primers. (2D) Quantification of titration shownin (2C). Each datapoint corresponds to the average of 3 PCR reactions;error bars represent s.e.m. (2E) 3C, singleplex and 6-plex LMA detectionof looping interactions between HS5 and the A gamma-globin gene HBG1.

FIG. 3A-3D shows that microarray and DNA sequencing analysis of 5Clibraries recapitulate 3C interaction profiles. (3A) 5C analysis ofhuman beta-globin locus HS5 chromatin interactions in K562 (ON) andGM06990 (OFF) cells detected by microarray. (3B) Conventional 3Canalysis of human beta-globin HS5 chromatin interactions. (3C) 5Canalysis of human betaglobin HS5 interactions as detected byquantitative DNA sequencing. (3D) Correlation between 3C and 5C humanbeta-globin locus profiles from K562 cells.

FIG. 4A-4C shows large-scale 5C analysis of the human beta-globin locus.(4A) Positions of forward (top) and reverse (bottom) 5C primers withinthe beta-globin locus (4B) Chromatin interaction profile of HS5 with a400 kb region surrounding the LCR. Physical interactions in K562 (ON)and GM06990 (OFF) cells were measured by 5C and microarrays (top), 3C(middle), and 5C and quantitative sequencing (bottom) analysis. (4C)Chromatin interaction profile of HS2/3/4 of the LCR with the 400 kbregion around the LCR as determined by microarray (top), andquantitative sequencing (bottom).

FIG. 5A-5B shows an analysis of the conformation of the gene desertcontrol region. (5A) Positions of alternating 5C forward (top) andreverse (bottom) primers throughout the gene desert control region. (5B)Chromatin interaction frequencies of the gene desert region asdetermined by conventional 3C (left panel), by 5C and microarrays(middle) and by 5C and quantitative sequencing (right panel).

FIG. 6 shows relative beta-globin mRNA levels in K562 cells.

FIG. 7 shows singleplex LMA titration of 3C and control libraries withhuman beta-globin 5C primers.

FIG. 8 shows quantitative detection of chromatin interactions usingmultiplex LMA with a mixture of 78 5C primers.

FIG. 9A-9B shows that 5C array hybridization signals are optimal withfeatures ranging from 38 to 48 nucleotides in length. (9A) Raw intensityof positive and half-site background hybridization signals (y-axis) wasdetermined for increasing probe lengths (x-axis). (9B) Percentage ofsignal due to half-site hybridization for each probe length wasestimated by calculating for each probe length the ratio of half-sitesbackground signal and specific signal.

FIG. 10A-10B shows analysis of CTCF binding to sites within thegamma-delta intergenic region (g/d1, g/d2) and the LCR (5′HS5) of thebeta-globin gene locus in K562 cells. (10A) Result of a representativequantitative duplex PCR with DNA recovered from a ChIP with antibodiesto CTCF. (10B) Quantitative results of two independent replicate ChIPexperiments.

FIG. 11A-11B shows a scatter plot analysis of 5C and 3C results from thehuman beta-globin profiles in K562 cells. (11A) Correlation between 5Cand 3C data shown in FIG. 3. (11B) Correlation between 5C and 3C datashown in FIG. 4.

FIG. 12 shows Table 3.

FIG. 13A-13B shows Table 5.

FIG. 14A-14B shows Table 6.

FIG. 15A-15B shows Table 7.

FIG. 16 shows a schematic of mapping of chromosome rearrangements using5C.

FIG. 17 shows exemplary diagnostic signatures of some embodiments of thepresent invention.

DEFINITIONS

As used herein, the term “genomic interaction library” refers to alibrary of nucleic acids generated by long range genomic interactions(e.g., chromatin looping chromatin looping, and interactions betweenelements located on different chromosomes). In some embodiments, genomicinteraction libraries are generated by chromosome conformation captureanalysis methods described herein (e.g., the 3C methods described byDekker, et al (2002) Science 295:1306-1311, herein incorporated byreference). In certain embodiments, genomic interaction libraries arefurther modified using the carbon copy ligation mediated amplificationmethods described herein. For example, in some embodiments, the genomicinteraction library is copied using ligation mediated amplification,followed by PCR amplification to generate a second genomic interactionlibrary.

As used herein, the term “long range genomic interaction” refers tophysical interactions between segments of nucleic acid (e.g., chromatin)located at large genomic distances (e.g., on different genes, atdifferent loci, or different chromosomes). Some long range genomicinteractions involve interactions between regulatory elements (e.g.,enhancers or repressors of gene expression). For example, somelong-range genomic interactions involve interactions between regulatoryelements and the gene being regulated. Other long-range interactionsinvolve interactions between genes. Still other long range interactionsinvolve elements that play general roles in chromosome conformation. Asused herein, the term “large genomic distances” refers to nucleic acidseparated by at least one function unit of nucleic acid (e.g., anintron, a gene, or a chromosome). Examples of nucleic acid separated bylarge genomic distances include, but are not limited to, nucleic acidslocated on different chromosomes, on different loci, or a gene and aregulatory region.

As used herein, the term “interaction frequency” refers to the frequencyat which two segment of nucleic acid (e.g., chromatin) interact. In someembodiments, interaction frequencies are calculated by dividing thenumber of sequences obtained by the number of sequences obtained from acontrol dataset that represents random interactions. In someembodiments, interaction frequencies are calculated by dividing thesignal obtained by hybridizing the second genomic interaction library toa microarray by the signal obtained by hybridization of a controllibrary to the microarray. In some embodiments, interaction frequenciesare normalized to control datasets.

The term “gene” refers to a DNA sequence that comprises control andcoding sequences necessary for the production of a polypeptide orprecursor. The polypeptide can be encoded by a full length codingsequence or by any portion of the coding sequence so long as the desiredenzymatic activity is retained. The term “gene” can also refer to a DNAsequence that is transcribed into an RNA molecule that does not encodefor a polypeptide.

The term “wild-type” refers to a gene or gene product that has thecharacteristics of that gene or gene product when isolated from anaturally occurring source. A wild-type gene is that which is mostfrequently observed in a population and is thus arbitrarily designed the“normal” or “wild-type” form of the gene. In contrast, the term“modified” or “mutant” refers to a gene or gene product that displaysmodifications in sequence and or functional properties (i.e., alteredcharacteristics) when compared to the wild-type gene or gene product. Itis noted that naturally-occurring mutants can be isolated; these areidentified by the fact that they have altered characteristics whencompared to the wild-type gene or gene product.

The term “oligonucleotide” as used herein is defined as a moleculecomprised of two or more deoxyribonucleotides or ribonucleotides,preferably more than three, and usually more than ten. The exact sizewill depend on many factors, which in turn depends on the ultimatefunction or use of the oligonucleotide. The oligonucleotide may begenerated in any manner, including chemical synthesis, DNA replication,reverse transcription, or a combination thereof.

Because mononucleotides are reacted to make oligonucleotides in a mannersuch that the 5′ phosphate of one mononucleotide pentose ring isattached to the 3′ oxygen of its neighbor in one direction via aphosphodiester linkage, an end of an oligonucleotide is referred to asthe “5′ end” if its 5′ phosphate is not linked to the 3′ oxygen of amononucleotide pentose ring and as the “3′ end” if its 3′ oxygen is notlinked to a 5′ phosphate of a subsequent mononucleotide pentose ring. Asused herein, a nucleic acid sequence, even if internal to a largeroligonucleotide, also may be the to have 5′ and 3′ ends.

When two different, non-overlapping oligonucleotides anneal to differentregions of the same linear complementary nucleic acid sequence, and the3′ end of one oligonucleotide points towards the 5′ end of the other,the former may be called the “upstream” oligonucleotide and the latterthe “downstream” oligonucleotide.

The term “primer” refers to an oligonucleotide which is capable ofacting as a point of initiation of synthesis when placed underconditions in which primer extension is initiated. An oligonucleotide“primer” may occur naturally, as in a purified restriction digest or maybe produced synthetically.

A primer is selected to have on its 3′ end a region that is“substantially” complementary to a strand of specific sequence of thetemplate. A primer must be sufficiently complementary to hybridize witha template strand for primer elongation to occur. A primer sequence neednot reflect the exact sequence of the template. For example, anon-complementary nucleotide fragment may be attached to the 5′ end ofthe primer, with the remainder of the primer sequence beingsubstantially complementary to the strand. Non-complementary bases orlonger sequences can be interspersed into the primer, provided that theprimer sequence has sufficient complementarity with the sequence of thetemplate to hybridize and thereby form a template primer complex forsynthesis of the extension product of the primer.

As used herein, the terms “hybridize” and “hybridization” refer to theannealing of a complementary sequence to the target nucleic acid (thesequence to be detected) through base pairing interaction (Marmur andLane, Proc. Natl. Acad. Sci. USA 46:453 [1960] and Doty et al., Proc.Natl. Acad. Sci. USA 46:461 [1960]). The terms “annealed” and“hybridized” are used interchangeably throughout, and are intended toencompass any specific and reproducible interaction between anoligonucleotide and a target nucleic acid, including binding of regionshaving only partial complementarity and binding interactions that makeuse of non-canonical interactions for stability and/or specificity.

The complement of a nucleic acid sequence as used herein refers to anoligonucleotide which, when aligned with the nucleic acid sequence suchthat the 5′ end of one sequence is paired with the 3′ end of the other,is in “antiparallel association.” Certain bases not commonly found innatural nucleic acids may be included in the nucleic acids of thepresent invention and include, for example, inosine and 7-deazaguanine.Complementarity need not be perfect; stable duplexes may containmismatched base pairs or unmatched bases. Those skilled in the art ofnucleic acid technology can determine duplex stability empiricallyconsidering a number of variables including, for example, the length ofthe oligonucleotide, base composition and sequence of theoligonucleotide, ionic strength and incidence of mismatched base pairs.

The term “non-canonical” as used in reference to nucleic acids indicatesinteractions other than standard, or “Watson-Crick” base pairing,including but not limited to G-T and G-U base pairs, Hoogsteininteractions, triplex structures, quadraplex aggregates, and multibasehydrogen bonding such as is observed within nucleic acid tertiarystructures, such as those found in tRNAs.

The stability of a nucleic acid duplex is measured by the meltingtemperature, or “T_(m).” The T_(m) of a particular nucleic acid duplexunder specified conditions is the temperature at which on average halfof the base pairs have disassociated.

The term “probe” as used herein refers to an oligonucleotide which formsa duplex structure or other complex with a sequence in another nucleicacid, due to complementarity or other means of reproducible attractiveinteraction, of at least one sequence in the probe with a sequence inthe other nucleic acid.

The term “label” as used herein refers to any atom or molecule which canbe used to provide a detectable (preferably quantifiable) signal, andwhich can be attached to a nucleic acid or protein. Labels may providesignals detectable by fluorescence, radioactivity, colorimetry,gravimetry, X-ray diffraction or absorption, magnetism, enzymaticactivity, and the like.

The term “sequence variation” as used herein refers to differences innucleic acid sequence between two nucleic acid templates. For example, awild-type structural gene and a mutant form of this wild-type structuralgene may vary in sequence by the presence of single base substitutionsand/or deletions or insertions of one or more nucleotides. These twoforms of the structural gene vary in sequence from one another. A secondmutant form of the structural gene may exist. This second mutant formvaries in sequence from both the wild-type gene and the first mutantform of the gene.

“Oligonucleotide primers matching or complementary to a gene sequence”refers to oligonucleotide primers capable of facilitating thetemplate-dependent synthesis of single or double-stranded nucleic acids.Oligonucleotide primers matching or complementary to a gene sequence maybe used in PCRs, RT-PCRs and the like. As noted above, anoligonucleotide primer need not be perfectly complementary to a targetor template sequence. A primer need only have a sufficient interactionwith the template that it can be extended by template-dependentsynthesis.

The term “substantially single-stranded” when used in reference to anucleic acid substrate means that the substrate molecule existsprimarily as a single strand of nucleic acid in contrast to adouble-stranded substrate which exists as two strands of nucleic acidwhich are held together by inter-strand base pairing interactions.

A “consensus gene sequence” refers to a gene sequence which is derivedby comparison of two or more gene sequences and which describes thenucleotides most often present in a given segment of the genes; theconsensus sequence is the canonical sequence.

The term “polymorphic locus” is a locus present in a population whichshows variation between members of the population (i.e., the most commonallele has a frequency of less than 0.95). In contrast, a “monomorphiclocus” is a genetic locus at little or no variations seen betweenmembers of the population (generally taken to be a locus at which themost common allele exceeds a frequency of 0.95 in the gene pool of thepopulation).

The terms “test compound” and “candidate compound” refer to any chemicalentity, pharmaceutical, drug, and the like that is a candidate for useto treat or prevent a disease, illness, sickness, or disorder of bodilyfunction. Test compounds comprise both known and potential therapeuticcompounds. A test compound can be determined to be therapeutic byscreening using the screening methods of the present invention.

As used herein, the term “sample” is used in its broadest sense. In onesense, it is meant to include a specimen or culture obtained from anysource, as well as biological and environmental samples. Biologicalsamples may be obtained from animals (including humans) and encompassfluids, solids, tissues, and gases. Biological samples include bloodproducts, such as plasma, serum and the like. Environmental samplesinclude environmental material such as surface matter, soil, water,crystals and industrial samples. Such examples are not however to beconstrued as limiting the sample types applicable to the presentinvention.

DETAILED DESCRIPTION OF THE INVENTION

The present invention relates to genomic analysis. In particular, thepresent invention provides methods and compositions for mapping genomicinteractions. Recent evidence indicates that regulatory elements can actover large genomic distances by engaging in direct physical interactionswith their target genes or with other elements (Chambeyron et al., CurrOpin Cell Biol. 16:256-262 (2004); de Laat et al., Chromosome Res.11:447-459 (2003); Dekker, J., Trends Biochem. Sci. 28: 277-280 (2003);and West et al., Hum Mol Genet. 14:R101-111 (2005). These observationsindicate that the genome is organized as a complex three-dimensionalnetwork that is determined by physical interactions between genes andelements. The present invention is not limited to a particularmechanism. Indeed, an understanding of the mechanism is not necessary topractice the present invention. Nonetheless, it is contemplated thatfunctional relationships between genes and regulatory elements can bedetermined by analysis of this network through mapping of chromatininteractions.

The development of 3C has greatly facilitated the detection and study ofcis- and trans-interactions between genes and regulatory elements.Experiments conducted during the development of the present inventionresulted in the development of 5C technology, an extension of 3C thatexpands the range of 3C applications by allowing comprehensive andlarge-scale mapping of chromatin interactions. Large-scale applicationof 5C provides information about relationships between genes andregulatory elements and can be used to identify novel regulatoryelements and to reveal higher-order chromosome structural features.

Physical interactions between elements can be detected with theChromosome Conformation Capture (3C) method (Dekker, J., Trends Biochem.Sci. 28: 277-280 (2003); Dekker et al., Science 295:1306-1311 (2002);Miele et al., “Mapping chromatin interactions by Chromosome ConformationCapture (3C). In: Current Protocols in Molecular Biology (ed. R. B. F.M. Ausubel, R. E. Kingston, D. D. Moore, J. G. Seidman, J. A. Smith, andK. Struhl), pp. 21.11.21-21.11-20. John Wiley & Sons, Hoboken, N.J.(2006); and Splinter et al., Methods Enzymol. 375:493-507 (2004). 3Cemploys formaldehyde cross-linking to covalently trap interactingchromatin segments throughout the genome. Interacting elements are thenrestriction enzyme digested and intra-molecularly ligated (FIG. 1A). Thefrequency with which two restriction fragments become ligated is ameasure for the frequency with which they interact in the nucleus(Dekker et al., (2002), supra).

3C was initially used to study the spatial organization of yeastchromosome III (Dekker et al., (2002), supra), and has since beenapplied to the analysis of several mammalian loci such as thebeta-globin (Palstra et al., Nat Genet 35:190-194 (2003); Tolhuis etal., Locus. Mol Cell 10:1453-1465 (2002); and Vakoc et al., Mol. Cell17:453-462 (2005)), the T-helper type 2 cytokine (Spilianakis et al.,Nat Immunol. 5:1017-1027 (2004)), the immunoglobulin kappa (Liu et al.,Mol Cell Biol. 25:3220-3231 (2005)), and the Igf2 imprinted locus(Murrell et al., Nat Genet. 36:889-893 (2004)). These studies revealeddirect interactions between enhancers and promoters of target genes,with the linking DNA looping outward. 3C was also used to detecttrans-interactions between yeast chromosomes (Dekker et al., (2002),supra) and between functionally related elements located on differentmouse chromosomes (Ling et al., Science 312:269-272 (2006); Spilianakiset al., Nature 435: 637-645 (2005); Xu et al., Science 311:1149-1152(2006)). Together, these studies indicate that long-range cis- and transinteractions play wide-spread roles in the regulation of the genome andthat 3C is a convenient approach to map this network of interactions. 3Cemploys PCR to detect individual chromatin interactions, which isparticularly suited for relatively small-scale studies focused at theanalysis of interactions between a set of candidate elements. However,PCR detection is not conducive to ab initio and large-scale mapping ofchromatin interactions. To overcome this problem, 3C libraries need tobe analyzed using a high-throughput detection method such as microarraysor DNA sequencing. The extreme complexity of the 3C library, and the lowrelative abundance of each specific ligation product make directlarge-scale analysis difficult.

The 3C method has been described in detail (Dekker, J., Nat Methods3:17-21 (2006); Dekker et al., (2002), supra; Miele et al., supra;Splinter et al., Methods Enzymol. 375:493-507 (2004); and Vakoc et al.,Mol. Cell 17:453-462 (2005)) and is illustrated in FIG. 1A. A 3Cexperiment generates a complex library of ligation products thatreflects all chromatin interactions that occur throughout the genome.The abundance of each specific ligation product in the library is ameasure for the frequency of interaction of the two corresponding loci.

In a typical 3C analysis individual interaction frequencies aredetermined by quantifying the formation of predicted “head-to-head”ligation products using semi quantitative PCR (FIG. 1A). As a PCRcontrol, a library is used that contains all ligation products inequimolar amounts. The control library is generated by mixing equimolaramounts of minimally overlapping BAC clones covering the genomic regionof interest (Dekker, J., Nat Methods 3:17-21 (2006); and Palstra et al.,Nat Genet 35:190-194 (2003)). This mixture is then digested and randomlyligated. Interaction frequencies are determined by calculating the ratioof PCR product obtained with the 3C library and the amount obtained withthe control library.

I. Chromosome Conformation Capture Carbon Copy (5C)

Experiments conducted during the course of development of the presentinvention resulted in the development of a novel methodology forlarge-scale parallel detection of chromatin interactions (e.g.,interaction between different chromosomes, different genes or differentloci). This method is called 3C-Carbon Copy or “5C”. 5C employs highlymultiplexed ligation-mediated amplification (LMA) to first “copy” andthen amplify parts of the 3C library followed by detection onmicroarrays, by quantitative DNA sequencing, or by other suitablemethods. 5C was developed and validated by analysis of the humanbeta-globin locus as well as a highly conserved genomic region locatedon human chromosome 16. However, the present invention is not limited tothe analysis of a particular chromosome or locus. Results indicated that5C quantitatively detects several known DNA looping interactions. 5Canalysis also identified a looping interaction between the beta-globinLocus Control Region (LCR) and the gamma-delta intergenic region.Previously, several lines of evidence have suggested that this regionplays a role in regulating the developmentally controlled switching fromgamma-globin expression in fetal cells to beta-globin expression inadult cells (Calzolari et al., EMBO J. 18: 949-958 (1999); Gribnau etal., Mol Cell. 5:377-386 (2000).

5C is widely applicable to determine the cis- and trans-connectivity ofregulatory elements throughout large genomic regions. In addition, insome embodiments, 5C experiments are designed so that completeinteraction maps can be generated for any large genomic region ofinterest, which can reveal locations of novel gene regulatory elementsand provide detailed insights into higher order chromosome folding. 5Ccan be used to detect, in a single reaction, a particular set ofchromatin interactions that can provide diagnostic, predictive, orprognostic information.

Exemplary embodiments of 5C are described below. 5C analysis finds manyuses and one skilled in the art recognizes that additional embodimentsand applications of 5C are within the scope of the present invention.

A. Outline of the 5C Technology

In some embodiments, 5C technology detects ligation products in 3Clibraries by multiplex LMA (FIG. 1A). LMA is widely used to detect andamplify specific target sequences using primer pairs that anneal next toeach other on the same DNA strand (FIG. 1) (Landegren et al., Science241: 1077-1080 (1988); Li et al., Nucleic Acids Res. 33:e168 (2005)).Only primers annealed next to each other can be ligated. Inclusion ofuniversal tails at the ends of 5C primers allows subsequentamplification of ligated primers. LMA-based approaches are quantitativeand can be performed at high levels of multiplexing using thousands ofprimers in a single reaction (Bibikova et al., Genome Res. 16:383-393(2005); Fan et al., Genome Res. 14:878-885 (2004); Hardenbol et al.,Genome Res. 15:269-275 (2005); Wang et al., Nucleic Acids Res. 33:e183(2005).

In some embodiments, to analyze chromatin interactions by 5C, a 3Clibrary is first generated using a conventional 3C method. A mixture of5C primers is then annealed onto the 3C library and ligated. Twoexemplary types of 5C primers are used: 5C forward and 5C reverseprimers. In preferred embodiments, these primers are designed so thatforward and reverse primers anneal across ligated junctions ofhead-to-head ligation products present in the 3C library (FIGS. 1A andB). 5C primers that are annealed next to each other are then ligated(e.g., with Taq ligase). This step generates a 5C library, which isamplified with universal PCR primers that anneal to the tails of the 5Cprimers.

In some embodiments, the products of ligation mediated amplification arefurther amplified using PCR. In preferred embodiments, the second, PCRbased amplification utilizes a single pair of primers that anneal to the5C ligation mediated amplification primers. In other embodiments, two ormore pairs of PCR primers may be used, preferably a limited number ofpairs (e.g., 2, 3, 4, 5) but preferably less than the number of ligationmediated amplification products.

Forward and reverse 5C primers are only ligated when both are annealedto a specific 3C ligation product. Therefore, the 3C library determineswhich 5C ligation products are generated and how frequently. As a resultthe 5C library is a quantitative “carbon copy” of a part of the 3Clibrary, as determined by the collection of 5C primers.

Forward and reverse 5C primers are designed to contain a unique sequencecorresponding to the sense and anti-sense strand of the 3′ end ofrestriction fragments, respectively (FIG. 1B). The primers also containuniversal tails for amplification (e.g., T7 at the 5′ end of forwardprimers and T3c at the 3′ end of reverse primers). The 5C technology isnot limited to use of these tails, other tail sequences can be useddependent on possible requirements of downstream detection methods. Toanalyze interactions between many restriction fragments, multipleforward and reverse primers are mixed together in the same multiplex 5Creaction. Since predicted forward and reverse primers of eachrestriction fragment are complementary, only one primer per fragment,either a forward or a reverse, is used in a given 5C experiment. In someembodiments, to facilitate ligation, all reverse primers arephosphorylated at their 5′ end. This 5C primer design allowssimultaneous amplification of all potential interactions between allrestriction fragments recognized by a forward primer and all thoserecognized by a reverse primer.

5C utilizes multiplexed ligation mediated amplification. Other assaysbased on LMA have successfully used many thousands of primers in asingle reaction. For example, methylation status of 1534 CpG sites wasassessed using a mixture of ˜6000 primers (Bibikova et al., Genome Res.16:383-393 (2005)). Another example is the use of highly multiplexed LMAwith up to 20,000 Molecular Inversion Probes in a single reaction todetect single nucleotide polymorphisms (SNPs) (Hardenbol et al., GenomeRes. 15:269-275 (2005); and Wang et al., Nucleic Acids Res. 33:e183(2005)). When 5C is performed at a similar level of multiplexing, e.g.using 10,000 5C primers in a single experiment, up to 25 milliondistinct chromatin interactions can be detected in parallel involving upto 40 Mb (10,000 4 kb restriction fragments) of DNA.

For highly multiplexed 5C analyses, it is preferred to carefully design5C primers. Nine 5C primers that were used to generate the 5C librariesanalyzed during experiments conducted during the development of thepresent invention perfectly recognized abundant interspersed repeats andthese primers were found to produce excessively large numbers ofligation products (see Table 5B). Thus, it is preferred that repeatedsequences be avoided.

In some embodiments, 5C applications are high-throughput applications.As described above, 5C methods are conducive to high levels ofmultiplexing. For example, in some embodiments, 10, preferably 100, evenmore preferably 500, still more preferably 1000, yet more preferably10,000 and even more preferably 100,000 primers are utilized in 5Capplications. In some embodiments, high-throughput methods such as theuse of microtiter plates are utilized for the simultaneous analysis ofmany different samples.

The products of a 5C reaction (5C library) may be analyzed using anysuitable method. In some exemplary embodiments of the present invention,5C libraries are detected using high throughput sequencing or microarray(See e.g., the Experimental section below). However, 5C libraries may bedetected using any DNA detection method including, but not limited to,bead based detection methods, mass spectrometry, and other detectionmethods known to those in the art.

5C methods are suitable for use with any number of cell types (e.g.,including, but not limited to, animal (e.g., human), plant, bacteria,fungi, and other organisms.

In some embodiments, 5C methods are automated. For example, in someembodiments, all of the steps of the 5C method (e.g., sample prep, 5Cmethods, and analysis of 5C libraries) are automated. In someembodiments, robotic methods are utilized.

B. Chromatin Looping in the Human Beta-Globin Locus

Experiments conducted during the course of developments of the presentinvention optimized the 5C approach by analysis of the human betaglobinlocus. However, the present invention is not limited to a particulargenomic region. One skilled in the relevant arts recognizes that the 5Ctechnology of the present invention finds use in the analysis of anynumber of loci or genomic regions. This locus was selected becauseseveral looping interactions have previously been detected by 3C as wellas by a second method, RNA-TRAP (Carter et al., Nat. Genet. 32: 623-626(2002); and Tolhuis et al., Locus. Mol Cell 10:1453-1465 (2002)). Thehuman beta-globin locus consists of five developmentally regulatedbetaglobin-like genes (epsilon (HBE); A gamma and G gamma (HBG1 andHBG2), delta HBD) and beta (HBB)), one pseudogene (Hbpsi), and a LocusControl Region (LCR) located upstream of the gene cluster (FIG. 2A). TheLCR is characterized by five DNAse I hypersensitive sites (HS1-5) and isrequired for tissue-specific and position independent expression ofdownstream beta-globin genes (Li et al., Blood 100:3077-3086 (2002); andStamatoyannopoulos, Exp Hematol. 33:256-271 (2005). Previous 3C analysisof the murine beta-globin locus revealed transcription factor-mediatedlooping interactions between the LCR and transcribed globin genes(Drissen et al., Genes Dev. 18:2485-2490 (2004); and Vakoc et al., Mol.Cell 17:453-462 (2005). The LCR was also found to interact with HSelements located upstream (HS-62.5/HS-60) and downstream (3′HS1) of thelocus (Tolhuis et al., Locus. Mol Cell 10:1453-1465 (2002).

Experiments conducted during the course of development of the presentinvention resulted in the detection of chromatin looping interactions inthe human beta-globin locus by 5C. The most prominent interaction wasobserved between the LCR and the expressed gamma-globin genesspecifically in K562 cells. In both K562 and GM06990 cells the LCR alsointeracted with the 3′HS1 element and a large domain located 50-100 kbupstream. The latter region corresponds to the region aroundHS-62.5/HS-60 in the murine locus that has been shown to interact withthe murine LCR (Tolhuis et al., Locus. Mol Cell 10:1453-1465 (2002).Similar long-range interactions between HSs were observed in the mouse.Although the functional significance of some of these interactions isnot well understood, the clustering of HSs is thought to create achromatin hub, or a specialized nuclear compartment dedicated to thetranscription of the beta-globin genes (de Laat et al., Chromosome Res.11:447-459 (2003); and Tolhuis et al., Locus. Mol Cell 10:1453-1465(2002)).

Several of the HSs in the beta-globin locus bind the insulator bindingprotein CTCF (Bulger et al., Mol Cell Biol. 23: 5234-5244 (2003);Farrell et al., Mol. Cell Biol. 22:3820-3831 (2002)) and for human HS5see FIG. 10) and this protein as been proposed to mediate theirinteractions and the formation of the chromatin hub (Patrinos et al.,Genes Dev. 18:1495-1509 (2004)). The pattern of interactions between theLCR and the rest of the beta-globin locus in the blood-derived GM06990cells is similar to those observed in K562, except that all interactionfrequencies are significantly lower. The present invention is notlimited to a particular mechanism. Indeed, an understanding of themechanism is not necessary to practice the present invention.Nonetheless, it is contemplated that the locus is in a “poised state” inwhich several chromatin looping interactions, e.g. those between the LCRand 3′ HS1 and the upstream HS elements are already established prior tobeta-globin expression, as has been suggested for the murine locus(Palstra et al., Nat Genet 35:190-194 (2003)).

The interaction profiles of HS5 and HS2/3/4 are very similar, exceptthat HS2/3/4 interacted more frequently with the beta-globin locusspecifically in K562 cells. This result is in agreement withobservations that HS2 and HS3 have the strongest enhancer activity(Fraser et al., Genes Dev. 7:106-113 (1993); and Peterson et al., ProcNatl Acad Sci USA. 93: 6605-6609 (1996)). In addition, RNA-TRAP foundthat the expressed globin gene interacted most strongly with HS2 (Carteret al., Nat. Genet. 32: 623-626 (2002)). A new chromatin loopinginteraction between the LCR and the region between the gamma- anddelta-globin genes was identified. This region has been implicated indevelopmental control of the beta-globin (Chakalova et al., Blood 105:2154-2160 (2005); and O'Neill et al., Proc Natl Acad Sci USA. 96:349-354(1999)). This region contains a promoter for a large intergenictranscript, whose expression may be related to activation of the adultbeta-globin gene (Gribnau et al., Mol Cell. 5:377-386 (2000)). Certainpatients that suffer from hereditary persistence of fetal hemoglobincarry deletions in this region and display defects in beta-globinexpression (Chakalova et al., Blood 105: 2154-2160 (2005)). In contrastto some other looping interactions in the locus, CTCF may not to play arole in the interaction between the LCR and the gamma-delta intergenicregions, CTCF binding to several sites within the intergenic region wasnot detected, despite the presence of several weak putative CTCF bindingsites (FIG. 10).

Results obtained with microarray detection, quantitative sequencing andsemiquantitative PCR are generally very comparable. Several differenceswere observed. First, the dynamic range of microarray detection wassmaller than that of quantitative sequencing, as has been observedbefore (Yuen et al., Nucleic Acids Res. 30:e48 (2002)). Second, smallquantitative differences were observed between the data sets obtained bymicroarray analysis, quantitative sequencing and semi-quantitative PCR,e.g. in the gamma-delta intergenic region (FIGS. 3 and 4). The presentinvention is not limited to a particular mechanism. Indeed, anunderstanding of the mechanism is not necessary to practice the presentinvention. Nonetheless, it is contemplated that these differencesreflect intrinsic biases in the detection methods or experimentalvariation between independently generated 5C libraries. DNA sequencingdisplays a larger dynamic range and obviates the need to design aspecific array for each genomic region of interest. Microarray analysisis currently more cost effective, particularly when a given genomicregion needs to be analyzed under a large number of differentconditions. 5C data obtained by DNA sequencing allowed an estimate ofthe background of the LMA-based approach. 451 interactions between thebeta-globin locus and the control gene desert region, which are locatedon different chromosomes, were quantitated. These interactions aredetected by forward primers located on one chromosome and reverseprimers located on the other and vice versa. There is no biologicalindication that the beta-globin locus and the gene desert region shouldpreferentially interact. Therefore it is contemplated that theseinter-chromosomal interaction frequencies correspond to backgroundsignals. Very low background interaction frequencies between the tworegions were detected (average interaction frequency 0.08, (s.e.m.=0.02)for K562 and 0.08 (s.e.m=0.01) for GM06990; Table 6), which is 75-foldlower than the interaction frequency between HS2/3/4 and the gammaglobingene in K562 cells. A few higher interaction frequencies were detectedthat could reflect true trans-interactions between the two genomicregions.

C. Mapping of Looping Interactions

Transcription regulation in higher eukaryotes is controlled byregulatory elements such as enhancers that are recognized bytranscription factors. In many cases regulatory elements can be locatedat distances up to several megabases from their target genes. Recentevidence shows that long-range control of gene expression can bemediated through direct physical interactions between genes and theseregulatory elements. In some embodiments, 5C is used for large-scalemapping of chromatin looping interactions between specific genomicelements of interest (e.g., the beta-globin locus). In certainembodiments, such studies are focused at mapping interactions between a“fixed” element, e.g. the LCR, and other restriction fragments locatedin cis or in trans in order to identify elements that it interacts with.5C allows simultaneous quantification of interaction profiles of manysuch “fixed” elements in parallel in a single reaction followed byanalysis on a custom-designed microarray or by direct quantitativesequencing. To do this, reverse 5C primers are designed for each fixedfragment of interest and forward 5C primers are designed for all otherrestriction fragments, as shown in FIG. 4A. This type of analysis allowsrapid detection of networks of interactions among multiple genes andregulatory elements throughout large segments of the genome.

D. Interaction Maps

In other embodiments, 5C analysis is used to generate dense interactionmaps that cover most or all potential interactions between all fragmentsof any genomic region. Dense interaction maps provide a global overviewof the conformation of a given genomic region. For example, when 5Cforward and reverse primers are designed for alternating restrictionfragments, as performed during the course of development of the presentinvention for the gene desert control region (FIG. 5), a relativelydense matrix of interaction frequencies is quickly obtained throughout agenomic region.

In some embodiments, several 5C analyses, each with a permutated 5Cprimer design scheme are performed to obtain partially overlappinginteraction matrices. Such analyzes provide a complete interaction mapfor a given genomic region, as interactions between two fragments thatare both recognized by forward primers or reverse primers cannot bedetected. When combined these maps yield complete interaction mapscontaining interaction frequencies of all pairs of restriction fragmentsthroughout a region of interest. Each row and column of such matricescorresponds to a “fixed” element experiment as described above.Generation of complete interaction matrices finds use as a discoverytool for unbiased detection of chromatin looping interactions betweenpreviously unannotated elements. Analysis of a matrix of interactionfrequencies provides global information regarding the general spatialconformation of a genomic region (Dekker et al., (2002)).

In some embodiments, the spatial conformation of a genomic region iscompared before and after induction of gene expression or silencing.Such embodiments find use in research, diagnostic, and therapeutic(e.g., drug screening) applications.

III. 5C Applications

The 5C methods of the present invention find use in a variety ofresearch, diagnostic, and clinical applications. Exemplary applicationsare described below.

A. Drug Screening

In some embodiments, the 5C technology of the present invention findsuse in drug screening applications. For example, in some embodiments, 5Canalysis is used to detect the three dimensional structure of a genomicregion in the presence and absence of a test compound. In otherembodiments, 5C is used to detect looping interactions betweenregulatory region in the presence and absence of test compounds. Suchembodiments find use in the study of drug function and in theidentification of compounds that alter the expression of a target gene.Such embodiments further find use in the identification and study ofcompounds that alter chromosome conformation and chromatin loopinginteractions.

B. Diagnostic Applications

In other embodiments, the 5C technology of the present invention findsuse in diagnostic applications. For example, in some embodiments, 5Cmethods are used to determine the status of chromosome conformation of agene of interest and identify cis- and trans chromatin interactions itis involved in. Certain disease states are characterized by aberrant(e.g., increased or decreased) gene expression that correlates at somestates of development or in some cell types with distinct pattern ofchromatin interactions. 5C methods thus find use in the diagnosis ofsuch disease states by detecting these patterns of chromatininteractions. In some embodiments, 5C is used to compare the interactionprofiles of genes in the activated or inactivated states with testsamples in order to determine the activation status of a gene.

In still other embodiments, 5C is used in the detection of variant(e.g., polymorphic) genes that have altered expression. For example, insome genes the presence of certain single nucleotide polymorphisms(SNPs) are associated with disease states or altered gene function(e.g., drug metabolism). In some embodiments, 5C is used to compare theinteraction profile of known SNPs with test samples in order todetermine the chromatin interactions of the variant gene, which can insome case be correlated with the activation status of a gene at somestage of development or in some cell types.

In yet other embodiments, 5C is used to detect patterns of chromatininteractions that are indicative of genomic rearrangements including,but not limited to, translocation, deletion, fusion, and inversion. Insome embodiments, 5C is used to compare the interaction profile of knowngene rearrangements with test samples in order to determine thechromatin interactions of the variant gene, which can in some case becorrelated with the activation status of a gene at some stage ofdevelopment or in some cell types. FIG. 16 shows an overview of thepredicted interaction profile for some exemplary genomic rearrangements.In some embodiments, interaction profiles to be used as controls areexperimentally generated using, for example, the methods of the presentinvention.

In additional embodiments, diagnostic signatures are utilized. In someembodiments, diagnostic signatures give information about diagnosticpredisposition and prognosis regarding specific diseases. For example,in some embodiments, diagnostic signatures predict future genomicrearrangements or detect chromosome conformation features (e.g., loopingor trans-interactions) associated with particular disease states orprognosis. Exemplary diagnostic signatures are shown in FIG. 17. In someembodiments, diagnostic signatures to be used as controls (e.g.,indicative of a given disease state or prognosis) are experimentallygenerated using, for example, the methods of the present invention.

C. Research Applications

In yet other embodiments, the 5C methods of the present invention finduse in research applications. Such applications include, but are notlimited to, the study of gene regulation in development anddifferentiation, the study of gene regulation in disease, the study ofgene regulation in drug metabolism, and the study of regulation ofvariant genes. In some embodiments, research applications utilizesamples from human subjects. In other embodiments, research applicationsutilize test samples from non-human animals (e.g., non-human mammals).In some embodiments, the non-human animals are transgenic animals.

D. Kits

In yet other embodiments, the present invention provides kits forperforming 5C. In some embodiments, the kits contain all of thecomponents necessary or sufficient for performing 5C analysis to detectparticular patterns of chromatin interactions in cells, including allcontrols, directions for performing assays, and any software foranalysis and presentation of results. In some embodiments, the kitscontain primers for performing 5C analysis. In some embodiments, thekits comprise all materials necessary or sufficient to perform 5C in asingle reaction and provide diagnostic, prognostic, or predictiveinformation (e.g., to a researcher or a clinician). In some embodiments,the kits comprise one or more of a polymerase (e.g., a thermostable DNApolymerase), a ligase (e.g., a thermostable ligase), primers foramplifying the products of a ligase chain reaction, buffers, controlreagents, sequencing reagents, solid surfaces for analysis, microarraysfor analysis, detection devices, software, instructions, and controlgenomic interaction libraries.

In some embodiments, the kits comprise all of the components forgenerating and utilizing a diagnostic signature (e.g., to provide adiagnosis or prognosis) or interaction profile of a sample. For Example,in some embodiments, the kits comprise control diagnostic signatures orinteraction profiles and software and/or instructions for comparing atest sample to the control.

EXPERIMENTAL

The present invention provides the following non-limiting examples tofurther describe certain contemplated embodiments of the presentinvention.

Example I General Laboratory Methods

BAC selection and control library preparation. A control library for thehuman betaglobin locus and gene desert regions (ENCODE regions ENm009and ENr313, respectively) was generated as described (Dekker, J., NatMethods 3:17-21 (2006); and Miele et al., supra). Briefly, an array ofbacterial artificial chromosomes (BACs) covering both genomic regionswas mixed, digested with EcoRI, and randomly ligated. In this study, theBAC arrays from the beta-globin locus and gene desert regions were mixedin a 4:1 ratio, to obtain strong signals for the beta-globin locus.Interaction frequencies were adjusted accordingly. The following 7 BACclones were used for the beta-globin region: CTC-775N13, RP11-715G8,CTD-3048C22, CTD3055E11, CTD-264317, CTD-3234J1, and RP11-589G14. A setof 4 BAC clones was selected to cover the 0.5 Mb gene desert region, andinclude RP11-197K24, RP11-609A13, RP11-454G21, and CTD-2133M23. BACclones were obtained from Invitrogen and the Children's Hospital OaklandResearch Institute (CHORI).

Cell culture and 3C analysis. The GM06990 cell line was derived from EBVtransformed B-lymphocytes and was obtained from Coriell CellRepositories (CCR). This cell line was cultured in Roswell Park MemorialInstitute medium 1640 (RPMI 1640) supplemented with 2 mM L-glutamine and15% fetal bovine serum (FBS). The K562 cell line was obtained from theAmerican Type Culture Collection (ATCC) and cultured in RPMI 1640supplemented with 2 mM L-glutamine and 10% FBS. Both cell lines weregrown at 37° C. in 5% CO₂ in the presence of 1% penicillin-streptomycin.3C analysis was performed with log phase GM06990 and K562 cells usingEcoRI as previously described (Dekker et al., Science 295:1306-1311(2002); Miele et al., supra; Vakoc et al., Mol. Cell 17:453-462 (2005)).Primer sequences are presented in Table 2. Real-time PCR quantification.Total RNA from log phase cells was isolated with the RNeasy Mini Kit asdescribed by the manufacturer (Qiagen). cDNA was synthesized witholigo(dT)₂₀ (Invitrogen) using the Omniscript Reverse Transcription Kit(Qiagen). Beta-globin transcripts were quantified by real-time PCR inthe presence of SYBR Green I stain (Molecular Probes). Specific humanbeta-globin primers used in this analysis are summarized in Table 1.

TABLE 1 GENE NAME PRIMER NAME PRIMER SEQUENCE (5′-3′) SEQ ID: 7HBE (EPSILON) FORWARD HBE GCTATTAAAAACATGGACAACCTC SEQ ID: 8 REVERSE HBECTCAGTGGTACTTATGGGCCAGG SEQ ID: 9 HBG1,2 (GAMMA 1,2) FORWARD HBG1AND2GCACCTGGATGATCTCAAGGG SEQ ID: 10 REVERSE HBG1AND2 GCTTGCAGAATAAAGCCTATCCSEQ ID: 11 HBpsi (PSEUDOGENE) FORWARD HBpsi CGGAAAAGCTGTTATGCTCACGGSEQ ID: 12 REVERSE HBpsi CCATCTAAAGGAGATGAGTTTTGGG SEQ ID: 13HBD (DELTA) FORWARD HBBandD GCCTTTAGTGATGGCCTGGCTCACC SEQ ID: 14REVERSE HBD GGAAACAGTCCAGGATCTCAATG SEQ ID: 15 HBB (BETA)FORWARD HBBandD GCCTTTAGTGATGGCCTGGCTCACC SEQ ID: 16 REVERSE HBBGGACAGCAAGAAAGCGAGCTTAGTG

TABLE 2 EcoR1 PRIMER FRAGMENT POSITION FRAGMENT NAME SEQUENCE (5′-3′)START END NAME 1. BETA-GLOBIN LOCUS (ENm009) CHROMOSOME 11 SEQ ID: 17GPF9 AGCACCATGGCATAGATTGAGGAGAAGG 5175467 5178450 131 SEQ ID: 18 GPF10TCTACACTCTCAGTCAGCCTATGGAACC 5178451 5184169 132 SEQ ID: 19 GPF11CAGGAGGTTGCCTTTGCTGTGGCTTTCGACCC 5184170 5165420 133 SEQ ID: 20 GPF14GAAAGCGAGCTTAGTGATACTTGTGGGC 5199709 5203476 136 SEQ ID: 21 GPF15GCTCCCACACTCCTAGACTCTTACAAAAGC 5203479 5209033 137 SEQ ID: 22 GPF17CGAAGTTCCTGGGAATATGCTAGTACAGAAC 5210845 5213155 139 SEQ ID: 23 GPF18GAATATTGAGATGATATATGCACAGAACAATGCC 5213108 5216308 140 SEQ ID: 24 GPF19CATGTCCTTTAATGGCCCTAAAAGTCATTCCC 5218307 5223331 141 SEQ ID: 25 GPF20GAAATGCTGTGACCAATCTGCACACTTGAGG 5223332 5225884 142 SEQ ID: 26 GPF21GCCTATCCTTGAAAGCTCTGAATCATGGGC 5225685 5226239 143 5229577 5231157 145SEQ ID: 27 GPF22 GGACCATTAACAGGGTAGGAAGTATTTATGG 5226240 5226681 144SEQ ID: 28 GPF23 GGGCATGTGGAAAACTCTGAGGCTGAGG 5228882 5119578 146SEQ ID: 29 GPF25 GAGAGTATCCAAAGTTATCTAAAGACAAAGAGAATC 5231158 5238141147 SEQ ID: 30 GPF27 GATATATAGACCAGTGGAACAGAACAGAAGCC 5236403 5239933149 SEQ ID: 31 GPF30 CAGGTACCACTAACAGCTCCTTCTTTCC 5251811 5245997 152SEQ ID: 32 GPF31 CCAGAAGTCTTCACCTGACTTAATGACTGCCC 5245998 5249755 153SEQ ID: 33 GPF32 GACATCAAGTATTTCTTGGATGCTGACCAGAGG 5249758 5256287 154SEQ ID: 34 GPF33 GACCTCTGCACTAGGAATGGAAGGTTAGCC 5258268 5256610 155SEQ ID: 35 GPF35 TCCTTGCAAATCATGAATAATGATCAATCGAGG 5267231 5270591 157SEQ ID: 36 GPF36 GGTGAGGAAATTGAGCCTTAGACAAGTTAAGG 5270592 5283838 158SEQ ID: 37 GPF38 GTACATAGTTAACCTGCTGCTTAGCTTATTTGC 5285015 5294021 160SEQ ID: 38 GPF41 GGCTGAGAGTCAAATCGAGAACACAATCC 5298304 5298001 163SEQ ID: 39 GPF47 GGCACCATGAAATTTATTCCTCATGAGGTCC 5310481 5317144 169SEQ ID: 40 GPF48 CTGCACATTCCAGGATCTATCTCCTACCTACG 5317145 5321683 170SEQ ID: 41 GPF55 GACCCATGTCTTTCTGTGTGTCTGCTAGTTC 5329232 5332065 177SEQ ID: 42 GPF56 GAAGGCTACAAGGGGATTTCTCAAGTAACTGC 5332056 5342910 178SEQ ID: 43 GPF57 GACATTGCTCAAGGTTAGCTAAAGATATG 5342911 5357573 179SEQ ID: 44 GPF58 GGTACACTGTTACAGTGACACTTTTCAC 5357574 5359095 180SEQ ID: 45 GPF59 GACAACAGAACCCCAGGCACAAAGAATCAGG 5359068 5363389 181SEQ ID: 46 GPF60 CCAATGATATGGTGGCTAAAAAAGTCAATCCC 5363370 5366062 182SEQ ID: 47 GPF64 GTGTAATGGGAAATCTATTGAGCCCTCTGTGC 5377148 5381758 186SEQ ID: 48 GPF67 CAATAGAGGAAAAGGAGGTACAGAAGCAC 5383984 5387568 189SEQ ID: 49 GPF76 CAGTTATCCAGTCTCAAAAGTGCAACTCTGTGC 5408494 5415062 195SEQ ID: 50 GPF83 GGAGTTTGCCTGCATCATCTCAAAAGCAGTG 5427609 5431239 205SEQ ID: 51 GPF91 GCACGGGAAAGTACCTGTAGTTACTAGGAAATG 5445764 5451773 213SEQ ID: 52 GPF92 GGGTACATGTGACTAGCATACACCTATTCAACC 5451774 5453800 214SEQ ID: 53 GPF93 GGGGACTTCCTAATTCCACCTCTTTGGAGC 5453301 5464851 215SEQ ID: 54 GPF95 GTGTCTGTGACTTACTAAGGAGAAAGTCAATTCC 5485402 5467640 217SEQ ID: 55 GPF96 GTGCTACACATATCACTGGTACTTAATACAACTG 5467641 5468314 218SEQ ID: 56 GPF97 GCTGTTTCAATTTGTGCTGAGGGAGACTCTC 5466315 5489998 219SEQ ID: 57 GPF100 CAAGACCCTGTTCATGCTATTTCACAGCTCC 5474899 5484552 222SEQ ID: 58 GPF101 CCATTCAGACCCACATTCAGCTACTTCCTG 5484553 5485089 223SEQ ID: 59 GPF102 GGGATATACAGTGGAGATGGCAGCAGCTGC 5486060 5491878 224SEQ ID: 60 GPF103 GATGAGTGAGGGTGATGCTAGGGCTTAGGATGC 5491679 5506192 225SEQ ID: 61 GPF104 CATAGGACACAACAGTGCCTGTAACACAG 5506193 5510183 226SEQ ID: 62 GPF105 CCTTCAGTGTTTGGCTCAATGTGGAACAAATCC 5510164 5511056 227SEQ ID: 63 GPF106 GTGGACAAGATGAGGTCAGTCATGGCCAGC 5518465 5523217 230SEQ ID: 64 GPF111 CCTAACATCTCACCTTTAGTAACTAGCAGAGCC 5525106 5531048 233SEQ ID: 65 GPF115 GGTGATTCATGAGCCTGGAACTGGTCCAACAG 5532760 5535890 2372. GENE DESERT REGION (ENr313) CHROMOSOME 16 SEQ ID: 66 GD2AGCTTCACCTCTCAAACTACAGGACTGG 60845578  60849115    2 SEQ ID: 67 GD3GTATACTCAGTTGAGCAGCCCATGACAC 60849116  60851238    3 SEQ ID: 68 GD5GTTCTCTGTCTTATAATTATGCTACAAGAATGAGG 60851792  60855935    5 SEQ ID: 69GD6 GTTTAAGACCCTCAGTATACTAGTCATAGAAGG 60855936  60856531    6 SEQ ID: 70GD7 GATGCCATTTGTTATCTTGTCTTGGCAGGTC 60858532  60888966    7 SEQ ID: 71GD8 GCAGCAAAGCAAACCAAAAGAACAACAGG 60868969  60873873    8 SEQ ID: 72 GD9GTGTCATGGAATCAAAGGTGAGTGAGGG 60873874  60878857    9 SEQ ID: 73 GD10TATAAAGCTGCAAGGGAGGGTTGACTG 60876858  60877889   10 SEQ ID: 74 GD12CGAGATGATGCTAACCTCTATGAACCTC 60880027  60880407   12 SEQ ID: 75 GD17GGCTGGCTGAGGTCATTCATGCAATCTT 60894707  60905483   17 SEQ ID: 76 GD18CCATTCCATCATACACCCTCATCTCACTGCC 60905484  60908417   18

5C primer design. Forward and reverse primers corresponding to the 3′end of EcoRI restriction fragments. Primer homology lengths varied from24 to 40 nucleotides and melting temperatures were centered at 72° C.The genomic uniqueness of all primers was verified with the SSAHAalgorithm (Hing et al., Genome Res. 11: 1725-1729 (2001). Forward 5Cprimers were designed to include a 5′ end tail that include (5′-3′): CTGfollowed by one Mme I restriction site (TCCAAC; SEQ ID NO:1), and amodified T7 Universal primer sequence (TAATACGACTCACTATAGCC; SEQ IDNO:2). Reverse 5C primers were designed to include a 3′ end tail thatinclude (5′-3′): a modified complementary T3 Universal sequence(TCCCTTTAGTGAGGGTTAATA; SEQ ID NO:3), one Mme I restriction site(GTCGGA; SEQ ID NO:4), followed by CTC. 5C forward and reverse primerseach contained half of the EcoRI restriction site and only the reverseprimers were phosphorylated at the 5′ end. All 5C primers are presentedin Table 3 (FIG. 12).

5C library preparation. 3C library (representing ˜150,000 genome copies)or control library (5 ng) was mixed with salmon testis DNA (Sigma) to atotal DNA mass of 1.5 μg, and with 1.7 fmol of each 5C primer in a finalvolume of 10 μl of annealing buffer (20 mM Tris-acetate pH 7.9, 50 mMpotassium acetate, 10 mM magnesium acetate, and 1 mM DTT). Samples weredenatured 5 min at 95° C. and annealed at 48° C. for 16 h. Annealedprimers were ligated 1 h at 48° C. by adding 20 μl of ligation buffer(25 mM Tris-HCl pH 7.6, 31.25 mM potassium acetate, 12.5 mM magnesiumacetate, 1.25 mM NAD, 12.5 mM DTT, 0.125% Triton X-100) containing 10units of Taq DNA ligase (NEB). Reactions were terminated by incubatingsamples at 65° C. for 10 min. 5C ligation products were amplified by PCRusing forward (T7 motif: CTGTCCAACTAATACGACTCACTATAGCC; SEQ ID NO:5) andreverse (T3 motif: GAGTCCGACTATTAACCCTCACTAAAGGGA; SEQ ID NO:6) primers.Six μl of ligation reaction was amplified with 10 pmol of each primer in25 μl PCR reactions (32 cycles of 30 s denaturing at 95° C., 30 sannealing at 60° C., and 30 s extension at 72° C.). 5C libraries werepurified with MinElute Reaction Cleanup Kit (Qiagen) to removeunincorporated primers and other contaminants as recommended by themanufacturer. Singleplex and 6-plex 5C analysis. 5C libraries from K562and GM06990 (each representing ˜150,000 genomes) or control libraries (5ng) were incubated with individual 5C primer pairs and processed asdescribed above, except that ligation reactions were amplified by 35 PCRcycles of 30 s denaturing at 95° C., 30 s annealing at 60° C., and 30 sextension at 72° C. Amplified 5C ligation products were resolved on 2%agarose gels and visualized with ethidium bromide (0.5 μg/ml). 6-plex 5Canalysis was performed by mixing 6 distinct 5C primers with 3C orcontrol libraries. Individual 5C ligation products of 6-plex sampleswere detected by PCR with specific internal PCR primers and, measured onagarose gels as described above. Linear range PCR detection of 5Cproducts was verified by two-fold serial dilution titrations ofmultiplex samples.

5C library microarray analysis. 5C libraries were prepared by performingmultiplex LMA with 78 5C primers, and amplified with a 5′-Cy3-labelledreverse PCR primer complementary to the common 3′ end tail sequence ofreverse 5C primers (Cy3-T3 motif). Maskless array synthesis andhybridization were carried out with 100 ng of amplified 5C libraries atNimbleGen Systems Inc. (Madison, Wis.) as previously described (Kim etal., Nature 436:876-880 (2005); Nuwaysir et al., Genome Res. 12:1749-1755 (2002); Selzer et al., Genes Chromosomes Cancer 44: 305-319(2005); and Singh-Gasson et al., Nat Biotechnol. 17:974-978 (1999)).Each array featured the sense strand of predicted 5C ligation products.The arrays also contained inter-region negative controls that were usedto identify problematic primers exhibiting high background signals dueto half-site non-specific cross-hybridization. The arrays contained 18replicates of increasing feature lengths ranging from 30 to 64nucleotides, which were used to identify optimal array probe lengths(FIG. 9). Arrays were scanned using a GenePix4000B scanner (AxonInstruments, Molecular Devices Corp., Sunnyvale, Calif.) at 5-μmresolution. Data from scanned images were extracted using NimbleScan 2.0extraction software (NimbleGen Systems, Inc.).

5C library high-throughput DNA sequencing analysis. 5C libraries weregenerated with 73 5C primers. Each library was amplified with 5′ endphosphorylated PCR primers and processed for single moleculepyrosequencing as previously described (Margulies et al., Nature437:376-380 (2005)). 550,189 sequence reads totaling million bases wereobtained using the GS20 platform developed by 454 Life Sciences Corp.The mean read length was 108 bases (mode, 112 bases). Each read wasblasted against all forward and reverse primers. For each sample, thenumber of reads that matched each of the 682 possible primer pairs (62forward×11 reverse) was counted. These combinations include 159 possibleinteractions in the beta-globin locus, 72 interactions in the genedesert region, and 451 inter-region interactions. Data are summarized inTables 4 and 5.

TABLE 4 K582 (ON) GM06990 (OFF) CONTROL TOTAL A. COMPLETE DATASET TOTAL197168 (100) 183029 (100) 195441 (100) 555658 (100) ASSIGNED 185494(94.1) 152355 (93.4) 178419 (91.3) 516288 (92.9) SINGLE PRIMER  10382(5.2)  9480 (5.8)  14378 (7.4)  34200 (6.2) UNCALLED  1331 (0.7)  1212(0.8)  2642 (1.3)  5185 (0.9) AMBIGUOUS    1 (<0.01)    2 (<0.01)    2(<0.01)    5 (<0.01) B. GENOMIC REGIONS TOTAL  63622 (32.2)  32835(20.1) 146426 (74.4) 241883 (43.5) BETA-GLOBIN  26098 (13.3)  10453(6.4)  69791 (35.7) 106340 (19.1) GENE DESERT  31180 (15.8)  18507(11.3)  3832 (2.0)  53519 (9.6) BETA  6346 (3.2)  3875 (2.4)  71803(36.7)  82024 (14.8) GLOBIN/GENE DESERT B. GENOMIC REGIONS TOTAL 121872(61.8) 119520 (73.3)  32993 (16.9) 274385 (49.4) BETA-GLOBIN  75031(38.0)  76992 (47.2)  17453 (8.9) 189478 (30.5) BETA  46841 (23.8) 42528 (26.1)  15540 (8.0) 104909 (15.9) GLOBIN/GENE DESERT

Example II 3C Verification of Human Beta-Globin Locus Chromatin Loops

The presence of chromatin loops in the human beta-globin locus was firstverified using the conventional 3C method. The locus was analyzed in theerythroleukemia cell line K562 and in the EBV-transformed lymphoblastoidcell line GM06990. K562 cells express high levels of epsilon andgamma-globin whereas GM06990 cells do not express the beta-globin locus(FIG. 11). 3C libraries were generated from both cell lines and acontrol library, which was generated using a series of minimallyoverlapping BAC clones. Interaction frequencies between the EcoRIfragment overlapping the HS5 element of the LCR and EcoRI restrictionfragments throughout the beta-globin locus were determined by PCR. Toallow direct quantitative comparison of interaction frequenciesdetermined in K562 cells and GM06990 cells interaction frequencies werenormalized using a set of 12 interaction frequencies detected in acontrol region, a conserved gene desert region on chromosome 16 (ENCODEregion ENr313; (ENCODE-consortium 2004)).

The normalized results are presented in FIG. 2B. In both cell lines HS5interacts frequently with adjacent DNA fragments. These interactionsreflect non-functional random collisions resulting from the intrinsicclose proximity of neighboring restriction fragments (Dekker, J., NatMethods 3:17-21 (2006); and Dekker et al., Science 295:1306-1311(2002)). The frequent random interactions between adjacent genomicfragments are likely dependent on local physical properties of thechromatin fiber and limit the ability to detect specific loopinginteractions between elements separated by small genomic distances (2-5kb) (Dekker 2006, supra; Gheldof et al., 2006). Random collisions arepredicted to decrease progressively for sites separated by increasinglylarge genomic distances. In K562 cells high interaction frequencies wereobserved specifically between the LCR and a restriction fragment located˜40 kb downstream and overlapping the A gamma-globin gene (HBG1),indicating the presence of a strong looping interaction. A frequentinteraction between the LCR and the 3′ HS1 element was also detected.This interaction was also present in GM06990 cells. Previous studies ofthe murine locus have shown that the analogous interaction also occursin non-expressing erythroid precursor cells (Palstra et al., Nat Genet35:190-194 (2003)). The analysis revealed less frequent randomcollisions between neighboring restriction fragments around the LCR inK562 cells as compared to GM06990 cells. Similar differences wereobserved in random collisions around the active and inactive FMR1promoter. These differences may reflect transcription-dependentdifferences in chromatin expansion or changes in sub-nuclearlocalization.

Based on this analysis is was concluded that the conformation of thehuman betaglobin locus is comparable to the murine locus with loopinginteractions between the LCR and 3′HS1 in both expressing andnon-expressing blood-derived cells. The interaction between the LCR andthe active A gamma-globin gene is only observed in globin expressingK562 cells.

Example III LMA Detection of 3C Ligation Products

Detection of chromatin loops in the beta-globin locus was used todevelop and optimize the 5C technology. LMA was first performed with asingle pair of 5C forward and reverse primers to verify that this methodcan quantitatively detect a ligation product in the context of a 3Clibrary. A 5C primer pair was designed that recognizes a ligationproduct that is formed by two adjacent restriction fragments located inthe gene desert control region. LMA was performed with this primer pairin the presence of increasing amounts of 3C library (generated fromGM06990 cells) and the formation of ligated forward and reverse primerswas quantified by PCR amplification with the pair of universal T7 and T3primers. Ligation of 5C primers is not observed when non-specific DNA ispresent, is dependent on the amount of the 3C library and requires Taqligase (FIGS. 2C and D). It was concluded that LMA can be used toquantitatively detect ligation products present in the 3C library.

Example IV LMA Detection of LCR-A γ-Globin Gene Looping

This example describes the use of singleplex LMA to quantitativelydetect chromatin looping interactions in the beta-globin locus.Interactions involving the LCR and three diagnostic fragments in thebeta-globin locus (indicated as bars in FIG. 2A) were detected: arestriction fragment located just downstream of the LCR, a restrictionfragment that overlaps the A gamma-globin gene HBG1 and a restrictionfragment located in between the delta- and beta-globin genes. A reverse5C primer was designed for the restriction fragment overlapping HS5 ofthe LCR and forward 5C primers for the three other fragments. The linearrange of 5C detection was determined with individual pairs of 5C primersin the presence of increasing amounts of 3C libraries (from K562 andGM06990 cells) or control library (FIG. 7).

Interaction frequencies between HS5 and the three other sites in thebeta-globin locus were determined by calculating the amount of ligated5C primers obtained with the 3C library and the amount obtained with thecontrol library. The interaction frequency between the two adjacentrestriction fragments located in the gene desert control region was usedfor normalization. Normalized interaction frequencies are shown in FIG.2E middle panel. The data obtained with LMA closely reproduced the 3Cdata, including the looping interaction between the LCR and the Agamma-globin gene. It was then tested whether the four interactionfrequencies studied here (three in the beta-globin locus and one in thecontrol region) can be detected and quantified in a single multiplex LMAreaction. LMA was performed with a mix of 6 5C primers and PCR withspecific primers was used to quantify the frequency with which specificpairs of 5C primers were ligated. Normalized interaction frequencieswere then calculated as described above. Similar results as withconventional 3C were obtained (FIG. 2E, right panel). Together, theseexperiments demonstrate that LMA can be used to quantitatively detectchromatin interactions.

Example V 5C Library Generation Using Multiplex LMA

Comprehensive 5C analysis of chromatin interactions throughout largegenomic regions utilizes high levels of multiplexing in combination witha high-throughput method for analysis of 5C libraries. LMA was tested athigher levels of multiplexing. Two high-throughput detections methodswere used to analyze 5C libraries: microarrays and quantitative DNAsequencing. 5C reverse primers were designed for each of the three EcoRIrestriction fragments that overlap the LCR and 5C forward primers for 55restriction fragments throughout a 400 kb region around the LCR. Thisprimer design allows detection of looping interactions between each ofthe three sections of the LCR and the surrounding chromatin in parallelin a single experiment (see below). Ten 5C forward and 10 5C reverseprimers were designed throughout a 100 kb region in the gene desertcontrol region. Forward and reverse primers were designed to recognizealternating restriction fragments. This primer design scheme allows thedetection of a matrix of interactions throughout the control region (seebelow).

LMA was performed with a mixture of all 78 5C primers using 3C librariesfrom K562 and GM06990 and the control library as templates. Each 5Clibrary contained up to 845 different 5C ligation products (the productsof 13 reverse primers and 65 forward primers). These products included165 possible interactions within the beta-globin locus, 100 interactionsthroughout the gene desert, and 590 interactions between the two genomicregions. It was verified that these 5C libraries representedquantitative copies of the selected fraction of the 3C libraries. To dothis, the same set of four interaction frequencies as in FIG. 2E wasanalyzed using specific PCR primers to quantify the abundance ofspecific 5C ligation products in the 5C libraries and normalizedinteraction frequencies between the LCR and the three positions of thebeta-globin locus were determined as described above. The 5C dataclosely reproduced the 3C interaction profile in both cell lines (FIG.8) with strong looping interactions between the LCR and the Agamma-globin gene HBG1 in K562 cells.

Example VI 5C Library Microarray Analysis

This example describes an analysis of microarray detection forcomprehensive analysis of the composition of 5C libraries. First, tofacilitate microarray detection we amplified the 5C libraries describedabove with Cy3-labeled universal primers. The labeled 5C libraries werethen hybridized to a custom designed microarray that can detect specific5C ligation products. Since each 5C product is composed of twohalf-sites, each corresponding to a 5C primer, cross-hybridization ofnon-specific 5C products can occur to probes that share one half-site.To assess half site cross-hybridization the microarray also containedprobes that recognize only one of the 78 5C primers present in thelibrary. To determine the optimal length of the microarray probes thatallows the least cross-hybridization, each probe was spotted with 18different lengths of half-sites ranging from 15 to 32 bases (total probelength ranging from 30 to 64 bases). 5C libraries were hybridized to thearray and specific and half-site hybridization was quantified. It wasfound that probes that are composed of two half-sites with a lengthranging from 19 to 24 bases displayed the lowest relative level of crosshybridization of half-sites (see FIG. 9). Data obtained with these sixfeature lengths was averaged and interaction frequencies were calculatedby dividing the hybridization signal obtained with a 5C library by thesignal obtained with the control library (see Table 7).

Example VII 5C Library Quantitative Sequencing Analysis

This example describes an analysis of the composition of 5C libraries byquantitative sequencing. 5C libraries are composed of linear DNAmolecules that each are around 100 bp long, which makes them ideallysuited for high-throughput single molecule pyrosequencing. Similar 5Clibraries as used for microarray detection were generated, except that 5of the 65 forward primers were left out. 5C and control libraries wereanalyzed using the GS20 platform developed by 454 Life Sciences Corp(Margulies et al., Nature 437:376-380 (2005). For each library at least160,000 sequence reads were obtained (Table 4). For each sequence it wasdetermined which pair of ligated 5C primers it represented and thenumber of times each specific 5C ligation product was sequenced wascounted (see Table 5 (FIG. 13)). As each ligation product was sequencedmany times (median count for intra-chromosomal interactions was 133 forK562, 53 for GM06990 and 134 for the control library) a quantitativedetermination of interaction frequencies was obtained. Interactionfrequencies were calculated by dividing the number of times a 5C productwas sequenced in a 5C library by the number of times it was sequenced inthe control library (Table 6 (FIG. 14)).

Example VIII Large-Scale 5C Analysis of the β-Globin Locus

This example describes the analysis of the interaction profiles of HS5throughout the 100 kb betaglobin locus as detected on the microarray(FIG. 3A) and by quantitative sequencing (FIG. 3C). For comparison, thesame interaction profile was also determined by conventional 3C and datawas normalized using interaction frequencies determined within thecontrol region (FIG. 3B). Both microarray detection and quantitativesequencing reproduced the overall 3C interaction profile of thebeta-globin locus in K562 and GM06990 cells. In all three datasets itwas found that the LCR specifically and strongly interacted with thegamma-globin genes in K562 cells. In both cell lines the loopinginteraction between the LCR and the 3′ HS1 element was detected. 3C and5C analyses also revealed strong interactions between the LCR and aregion located between the gamma- and delta-globin genes in K562 cells.This region contains the beta-globin pseudogene, which is weaklyexpressed in K562 cells, but is silent in GM06990, and the initiationsite for an intergenic transcript (Gribnau et al., Mol Cell. 5:377-386(2000)).

The 5C and 3C datasets were compared directly by calculating for eachpair of interacting fragments the fold difference between theirinteraction frequencies as determined by 5C and 3C. The difference in 5Cdata obtained by microarray detection and conventional 3C is generallyless than 2-fold (FIG. 3D). Larger differences were observed when 5Cdata obtained by quantitative sequencing was compared to 3C data (FIG.3D). The present invention is not limited to a particular mechanism.Indeed, an understanding of the mechanism is not necessary to practicethe present invention. Nonetheless, it is contemplated that this may bedue to the fact that the dynamic range of quantitative sequencing ishigher than that of semiquantitative PCR or microarrays, which resultsin higher peaks and lower valleys in the profile obtained by sequencingas compared to semi-quantitative PCR. The correlation between 5C and 3Cdata was determined directly by plotting the data in a scatter plot(FIG. 11). A high degree of correlation (r2=0.73) was found for thedataset obtained by sequencing as well as for the dataset obtained onthe microarray (r2=0.75)).

Taken together, these results show that 5C in conjunction withmicroarray detection or quantitative sequencing is a powerfulmethodology to quantitatively detect chromatin interactions in ahigh-throughput setting.

Example IX Interactions Between HS5 and Upstream Elements

In the mouse the LCR interacts with HS elements (HS-62.5/HS-60) locatedup to 40 kb upstream of the LCR (Palstra et al., Nat Genet 35:190-194(2003); Tolhuis et al., Locus. Mol Cell 10:1453-1465 (2002)). It is notknown if functionally equivalent elements are present in this region ofthe human genome. It has been noted that olfactory receptor geneslocated approximately 90 kb upstream of the LCR are orthologous to oneslocated 40 kb upstream of the murine locus (Bulger et al. 2000),indicating that these regions are related. In addition, the murineHS-62.5/HS-60 element is embedded in a sequence that is similar to asequence located ˜90 kb upstream of the human LCR (Bulger et al. 2003).These observations indicate that the region located ˜90 kb upstream ofthe human LCR is orthologous to the region located ˜40 kb upstream ofthe murine locus suggesting that this region may also interact with theLCR in human cells. To assess in an unbiased fashion whether the LCRinteracts with any upstream elements in the human locus the 5Cexperiment described above was designed to include analysis of a largeregion located upstream of the LCR. The interaction profiles obtained bymicroarray detection and quantitative sequencing of HS5 with a region upto 280 kb upstream of the LCR were analyzed. Both datasets showed thatinteractions throughout this region are generally much lower than thoseobserved between the LCR and the beta-globin locus. In both cell lineselevated interaction frequencies were detected throughout a large domainlocated 50 to 100 kb upstream of the LCR (FIG. 4A). This result wasconfirmed by conventional 3C (FIG. 4A, compare top, middle and bottompanels). This region contains three olfactory receptor genes andmultiple HS sites (Bulger et al., Mol Cell Biol. 23: 5234-5244 (2003)).

The present invention is not limited to a particular mechanism. Indeed,an understanding of the mechanism is not necessary to practice thepresent invention. Nonetheless, it is contemplated that these resultssuggest that the region located 50-100 kb upstream of the LCR in thehuman genome is in relative close proximity of the LCR and therefore isfunctionally equivalent to the genomic region located 40 kb upstream ofthe LCR in the murine locus. In addition, these results illustrate thatlarge-scale mapping of interactions using 5C can greatly facilitate thediscovery of the locations of novel putative regulatory elements.

Example X Parallel Analysis of Multiple Interaction Profiles

A major advantage of 5C is the fact that interactions between multipleelements of interest and other genomic elements can be analyzed inparallel in a single experiment. The 5C experiment described here wasdesigned to illustrate this aspect of the methodology. As describedabove 5C forward and reverse primers were designed to allow simultaneousdetection of interaction profiles of each of the three sub-sections ofthe LCR with the 400 kb surrounding chromatin. Data obtained bymicroarray analysis and quantitative sequencing of 5C libraries showedthat the interaction profile of the restriction fragment overlappingHS2/3/4 of the LCR fragment is very similar to that of HS5 (FIG. 4C). InK562 cells HS2/3/4 interacted more frequently with sites throughout thebeta-globin locus than HS5, suggesting that these HSs may contributemost to the formation of the chromatin loops with LCR. 5C analysis ofthe LCR 3′ end, which contains HS1, did not yield sufficient levels ofligation products to obtain significant number of sequence reads.Analysis of microarray hybridization signals confirmed the low levels ofligation products formed with the 5C primer for HS1, but the generalpatterns of interaction frequencies for K452 and GM06990 cells wereconsistent with the patterns obtained for HS5 and HS2/3/4 (Table 7 (FIG.15)).

Example XI Large-Scale 5C Analysis of the Gene Desert Control Region

The 5C analysis of the beta-globin locus was focused at the mapping ofinteractions between a fixed regulatory element, the LCR, and thesurrounding chromatin. 5C experiments can also be designed so that amore global dataset is obtained, which is particularly useful when thepositions of regulatory elements are poorly defined. This exampleprovides an example of an alternative 5C primer design scheme thatprovides insights into the general spatial conformation of a genomicregion. The 5C analysis described above included 10 forward and 10reverse 5C primers for restriction fragments located in the gene desertcontrol region (FIG. 5A). Forward and reverse primers were designed foralternating restriction fragments (FIG. 5A). Combined these primersdetected chromatin interactions throughout the region. Interactionfrequencies determined by microarray analysis and quantitativesequencing were plotted against the genomic distance between theinteracting restriction fragments. In both cell lines, interactionfrequencies were found to decrease with increasing genomic distance(FIG. 5B). Similar results were obtained by conventional 3C analysis.The graphs in FIG. 5 do not reveal where along the chromosome particularinteraction frequencies were measured. To better illustrate that aglobal interaction map is obtained, interaction frequencies betweenforward and reverse 5C primers as two-dimensional heatmaps in which thecolor of each square is an indication of the interaction frequencybetween restriction fragments were generated. Interaction frequenciesdisplayed along the diagonal reflect interactions between fragmentslocated close together along the chromosome. The overall pattern ofinteractions observed in this gene desert region is very different fromthat observed in the beta-globin locus and is consistent with an overalllinear conformation of the chromatin fiber (Dekker, (2006), supra;Dekker et al., (2002), supra; and Rippe, Trends Biochem Sci 26:733-740(2001).

Example XII Chromatin Immunoprecipitation

Chromatin immunoprecipitation (ChIP) assays were performed essentiallyas described in Gombert et al., (Mol Cell Biol. 23: 9338-9348 (2003))with minor modifications. Chromatin from exponentially growing K562cells (8×107 total) was crosslinked in 1% formaldehyde for 3 minutes atroom temperature. After addition of glycin to a final concentration of0.125M, cells were washed and sonicated with 10 cycles (30 sec each) inan ethanolice bath. The average size of DNA in the cross-linkedchromatin was approximately 200 to 500 bp. Chromatin from the equivalentof 1×10⁷ cells was incubated with polyclonal antibodies to CTCF(purchased from Upstate Biotechnology) for 4 hours. DNA fromimmunoprecipitated and washed chromatin was recovered as described in(Gombert et al. 2003, supra), and subjected to PCR with primers listedin Table 8.

Results are shown in FIG. 10. Analysis of CTCF binding to sites withinthe gamma-delta intergenic region (g/d1, g/d2) and the LCR (5′HS5) ofthe beta-globin gene locus in K562 cells. (A) Result of a representativequantitative duplex PCR with DNA recovered from a ChIP with antibodiesto CTCF. DNA from input chromatin (Input) and immunoprecipitatedchromatin (CTCF-CMP) was PCR-amplified with primers specific for thehuman myotonic dystrophy gene (DM1), the LCR (5′HS2), or the gamma-deltaintergenic region (g/d1 and g/d2) of the beta-globin gene locus.Quantitative PCR reactions also contained a reference primer setspecific for the promoter-region of the beta-globin gene (beta-ref) fornormalization. (B) Quantitative results of two independent replicateChIP experiments. Normalized signals (Y-axis) represent the ratio ofsignals obtained by experimental primer sets (DM1, g/d1, g/d2, 5′HS5)and reference primer set (beta-ref).

TABLE 8 TPCR AMPLICON PRIMER NAME SEQUENCE (5′-3′) SIZE (bp) DM1 FORWARDCAGTTCACAACCGCTCCGAG 142 DM1 REVERSE GCAGCATTCCCGGCTACAAG g/c1 FORWARDGGAGATCAGCACCTTCTTGC 130 g/c1 REVERSE ATCCCACAGTCTCCTGGTTG g/c2 FORWARDGTCAAGGGTGGGTTGTGACT 154 g/c2 REVERSE GAAAATGGAGGGGAAGGAAG 5HS5 FORWARDTCCAGATGTCCTGTCCCTGT 148 5HS5 REVERSE GCTGAAGCTGCTGTTATGACC

All publications and patents mentioned in the above specification areherein incorporated by reference. Various modifications and variationsof the described method and system of the invention will be apparent tothose skilled in the art without departing from the scope and spirit ofthe invention. Although the invention has been described in connectionwith specific preferred embodiments, it should be understood that theinvention as claimed should not be unduly limited to such specificembodiments. Indeed, various modifications of the described modes forcarrying out the invention that are obvious to those skilled in therelevant fields are intended to be within the scope of the followingclaims.

What is claimed is:
 1. A method, comprising; a) contactingdeoxyribonucleic acid with a cross-linking agent under conditions suchthat interacting chromatin segments are cross-linked; b) digesting saidcross-linked chromatin segments to generate digested chromatin segments;c) ligating said digested chromatin segments to generate a first genomicinteraction library comprising a plurality of ligation products; d)contacting said plurality of ligation products with a plurality ofunique primers under conditions such that ligation mediatedamplification generates a second genomic interaction library that is acopy of a part of said first genomic interaction library; and e)amplifying said second genomic library with a single pair of PCRprimers.
 2. The method of claim 1, wherein said plurality of uniqueprimer pairs comprises at least 10 unique primer pairs.
 3. The method ofclaim 1, wherein said plurality of unique primer pairs comprises atleast 100 unique primer pairs.
 4. The method of claim 1, wherein saidplurality of unique primer pairs comprises at least 500 unique primerpairs.
 5. The method of claim 1, wherein said plurality of uniqueprimers pairs comprises at least 1000 unique primer pairs.
 6. The methodof claim 1, wherein said plurality of unique primers pairs comprises atleast 10,000 unique primer pairs.
 7. The method of claim 1, wherein saidplurality of unique primers pairs comprises at least 100,000 uniqueprimer pairs.
 8. The method of claim 1, wherein said genomic interactionlibrary comprises ligated deoxyribonucleic acids approximately 100 bpsin length.
 9. The method of claim 1, wherein said second interactionlibrary is representative of long-range genomic interactions.
 10. Themethod of claim 9, wherein said long-range genomic interactions compriseinteraction of activators or repressors of gene expression with a gene.11. The method of claim 9, wherein said long-range genomic interactionscomprise interaction of chromatin on different chromosomes.
 12. Themethod of claim 1, wherein said deoxyribonucleic acid is derived from atleast one cell.
 13. The method of claim 12, wherein said at least onecell is selected from the group consisting of an animal cell, abacterial cell and a plant cell.
 14. The method of claim 9, furthercomprising the step of calculating interaction frequencies for said longrange genomic interactions.
 15. The method of claim 1, wherein saidgenomic interaction library has one or more variant genes selected fromat least one of the group consisting of polymorphisms, genomicdeletions, genomic fusions, genomic translocations, and genomicinversions.
 16. The method of claim 1, wherein said single pair of PCRprimers is a single pair of universal primers.
 17. The method of claim1, further comprising step (f) analyzing said amplified second genomiclibrary with a high-throughput application.
 18. The method of claim 17,wherein said high throughput application comprises sequencing.
 19. Themethod of claim 17, wherein said high throughput application isperformed using a microarray.